Methods and systems for collaborative demand planning and replenishment

ABSTRACT

The present invention provides systems and methods for generating replenishment orders for products sold to a multi-store retailer. Store trait data is mapped with planogram information and the mapped data is used to generate a fixture level safety stock for a particular product in a particular store. The fixture level safety stock triggers fixture level replenishment orders.

This application is a divisional application of U.S. patent applicationSer. No. 11/829,779 filed on Jul. 27, 2007 now issued U.S. Pat. No.7,848,967 which is a divisional application of U.S. patent applicationSer. No. 10/932,672 filed on Sep. 1, 2004 which claims the benefit ofU.S. Provisional Application No. 60/500,425 filed on Sep. 4, 2003, allof which are incorporated herein by reference in their entirety.

FIELD OF THE INVENTION

The field of the invention is vendor managed inventory.

BACKGROUND OF THE INVENTION

Vendor managed inventory (VMI) systems generally allow product suppliersto manage inventory of product retailers. VMI systems first appeared inthe 1980s with the goal of shifting some of the burden of productreplenishment away from retailers and into the hands of the vendors.Early VMI systems used point of sale (POS) data to decrement an onhandquantity (i.e. perpetual inventory) until that decremented quantitydropped below a safety stock level. Once the quantity dropped below thesafety stock level, an order was generated if economic order quantityrestrictions were met.

Today, most large retailers do not stock excess inventory primarilybecause of high inventory carrying cost. As a result, it is particularlyimportant that replenishment of items on shelves be sufficient to meetdemand yet not be overly abundant so as to cause excess inventory.Ideally, there would be no excess inventory, but there would always bean item available to meet demand.

In attempting to meet this ideal, VMI systems examine sales data at theproduct level. This is problematic, however, because the demand for aparticular product on a particular shelf can vary significantly amongstores and even among shelves in the same store. In traditional VMIsystems, suppliers had no visibility or insight into shelf levelinventories in part because the information was not available, was toovoluminous to handle, and was too dynamic. Thus, replenishmentcalculations often left too much inventory on some shelves and toolittle inventory on others. The problems were exacerbated by the factthat a particular item could sell well in one area of a store and poorlyin another area of the same store.

SUMMARY OF THE INVENTION

The present invention provides systems and methods for generatingreplenishment orders for products sold to a multi-store retailer. Storetrait data is mapped with planogram information and the mapped data isused to generate a fixture level safety stock for a particular productin a particular store. The fixture level safety stock triggers fixturelevel replenishment orders.

In another aspect, a vendor managed inventory (VMI) system comprises aremote server in communication over a public packet switched networkwith a local retailer system. The retailer system stores planograminformation and store trait data which is received by the VMI system.The VMI system then maps the store trait data with planogram informationin order to derive a fixture level safety stock for a particular productfor a particular store. The safety stock is used as part of a trigger togenerate replenishment orders.

Various objects, features, aspects and advantages of the presentinvention will become more apparent from the following detaileddescription of preferred embodiments of the invention, along with theaccompanying drawings in which like numerals represent like components.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic of a collaborative demand planning andreplenishment (CDPR) system.

FIG. 2 is a store trait data listing.

FIG. 3 is a planogram information listing.

FIG. 4 is a listing of mapped planogram information and store traitdata.

FIG. 5 is a schematic of a safety stock and replenishment calculationsystem.

DETAILED DESCRIPTION

Referring to FIG. 1, a CDPR system 100 generally comprises a retailerinventory management module 110, a network 120, and a vendor managedinventory module 130.

Retailer inventory management module 110 includes on-hand inventory(perpetual inventory) amounts by product within shelf within store.Planograms are transmitted over a network 120 (e.g. the Internet) to avendor managed inventory system (i.e. CDPR system). Preferably, filesare transferred using file transfer protocol (FTP), however variousother protocols and technologies can be used to transfer planogram data112 including transfers using HTTP. It should also be recognized thatall transmissions discussed herein can be accomplished using wiredand/or wireless communication paths.

Planograms are used by retailers and others to design and allocate shelfspace in a store. A planogram is generally depicted graphically, but inany case typically includes the following information: product code,product vendor, fixture (i.e. location or shelf), fixture capacity, andeffective date. FIG. 3 depicts some exemplary planogram information.Note, however, that planogram information does not include storeidentification.

For purposes of this specification, a fixture is the lowest levellocation of a product on a shelf. For example, if a retailer creates aplanogram showing product configurations only to a shelf level ofdetail, the word “fixture” is synonymous with shelf. In fact, throughoutmuch of this specification, the words fixture and shelf areinterchangeable. However, if a shelf is further divided into multipleareas, a fixture is synonymous with a particular area on the shelf.Thus, a fixture is always the most detailed location of a product. Itshould be noted that shelf and/or fixture configurations generallyremain consistent across all stores of a multi-store retailer.

Planogram information is generally entered by a retail level employeesuch as a merchandising manager. In most cases, the merchandisingmanager will derive planograms as a result of sales data, trends, andother dynamic information, and as such, planogram information can changevery quickly. Not only can fixtures be added, deleted, and changed, butstores may open and close. All this adds to the dynamic nature ofplanograms. Because current planogram information is important tocalculations made in a VMI system, it is contemplated that future (i.e.effective date in the future) as well as current planograms will beaccepted and thereafter implemented based on the effective date. Inaddition, preferred embodiments will store historical planograms for usein analyzing particular store configurations. Performance datacalculated at the vendor level is envisaged to be transmitted back tothe retailer for input into store trait and planogram configurations.

The vendor managed inventory module 130 or CDPR module is generallycontrolled by a product provider (e.g. supplier, vendor, manufacturer,distributor, and so on), and the product provider is responsible formaintaining the data integrity and functional aspects of the system. Inthe context of this application, a vendor or product provider is anentity that provides a product that is directly or indirectly sold at aretail level.

The CDPR module uses the planogram information as well as otherinformation including demographic, psychographic, and trend data tocreate forecasting data, rate of return data, performance data, andother outputs that are transmitted to the retail level inventorysystems. Significantly, such information is used by the product providerto replenish inventory on a timely basis. It is the collaborationbetween the retail level system and the vendor level system that resultsin the exchange of valuable information. For example, the CDPR module isalso capable of making recommendations to the retail level entity bytaking into consideration past and future planogram information. Theserecommendations are generally contemplated to include factors thataccount for trends in product demand down to the store level or even thefixture level. Thus, a recommendation for one store will usually bedifferent from that of another store because of demographic andpsychogaphic differences. Moreover, recommendations can vary dependingon the location of a fixture in a store.

Past, current, and future planogram information is accepted by the VMImodule. As such, the effective date of a planogram is important. A storemay, for instance, transmit a series of planograms having successiveeffective dates as well as varying capacities, locations, and so on. TheCDPR module is receptive to multiple planograms and can even accept andincorporate changes to planograms in a real-time manner.

Drawing your attention now to FIG. 2, a store trait data listing 200generally includes the following fields: store identification 210 (e.g.store number), fixture 220, and effective date 230. Store trait databasically represents the configuration of a store in terms of itsfixtures. It should be noted that a multi-store retailer has stores thatare divided up into fixtures; so, for example, store 1 has fixtures A,B, and F. Obviously, FIG. 2 shows exemplary data and many more fixtureswould likely be included in most retail stores. Still, with reference toFIG. 2, one can see that fixtures B and F are part of the configurationof stores 1 and 2 and fixture B is part of the configuration of store 3.

FIG. 3 depicts planogram information 300 including fields for productcode 310, fixture 320, and fixture capacity 330. Focusing on FIG. 3, onecan see that the capacity for item WP1432 is 25 on fixture B and 10 onfixture F.

In FIG. 4, a listing of mapped data 400 includes store identification410, product code 420, fixture 430, and fixture capacity 440. Of course,other fields such as effective data may be included in a listing ofmapped data, though not depicted for purposes of FIG. 4. It iscontemplated that mapping includes a step of matching store trait dataand planogram information by fixture identifier. Using the mapped data,it becomes clear that stores 1 and 2 require 35 WP1432 while store 3requires only 25. It should be recognized that because capacities are atthe fixture level, safety stock and replenishment amounts can becalculated by fixture thereby reducing the incidence of excess orinsufficient inventory and at the same time increasing good will towardthe retailer.

Now, with regard to FIG. 5, a safety stock and replenishment calculationsystem 500 generally comprises a VMI system 510 that calculates a safetystock 520 which, in turn, is input to a replenishment amount 530.

A purchasing manager 550 uses store sales figures, trends, demographics,psychographics, and so on to determine a fixture configuration for astore. It should be recognized that a purchasing manager is not requiredas there are other entities and automated methods that can be used todetermine fixture configurations. In any case, fixture configuration bystore is reflected in the store trait data 555.

On the other side of the equation, a merchandising manager 560 createsplanogram information 565 based on product information as well as salesinformation, space limitations, and store layouts. Again, another entityor automated method may be substituted for a merchandising manager inless preferred embodiments.

Both the store trait data 555 and planogram information 565 are input tothe vendor managed inventory system 510. It is contemplated thattransmission of store trait data and planogram information may utilizevarious channels of communication including most especially those thatuse the Internet. As planogram information and store trait data is oftenin a spread sheet format (e.g. Microsoft® Excel), a preferred VMI systemallows for import of such files formats.

VMI system 510 receives demographic data and psychogaphic data 570including ages, incomes, socio-economic data and so on. Such demographicand psychogaphic data can be applied to the capacity for a productbefore selecting a safety stock value. VMI system 510 also receivesinformation which enables it to calculate economic order quantity (EOQ)rules 575. One of skill in the art will recognize that EOQ rules maytake into consideration lead times and economic order quantities inaddition to other information.

VMI system 510 also receives point of sale data 580 preferably includingfixture level sales data. Point of sale data 580 includes product id,store number, and quantity sold and may also include perpetual inventoryamounts and a fixture identification, though the latter two items ofdata are not included in some embodiments. In a preferred class ofembodiments, fixture level point of sales (POS) data is applied tocurrent fixture level capacity which was received as part of theplanogram information. It should be pointed out here that in most cases,current fixture level capacity is based on the effective date that wasalso received as part of the planogram information. Collecting point ofsale data at a fixture level is generally accomplished by affixing afixture identifier on or in each product. Preferably the fixture levelidentifier is in the form of an RFID chip which is read by the point ofsale system in order to indicate a sale of a product from a particularfixture.

A further aspect of the inventive matter is the inclusion of POS data tothe day of the week. The following example, based on the data in FIG. 4,elucidates this aspect. Store 1 has capacity of 25 for product WP1432 onfixture B. Assume that a safety stock of 13 was calculated based on theinventive concepts provided herein. When the perpetual quantity ofWP1432 on fixture B in store 1 dips below 13, generation of a fixturelevel replenishment order is triggered. If the replenishment order isgenerated on a Thursday and the lead time is 3 days, the VMI system isadvantageously programmed to look at fixture level sales history for thedays of Friday, Saturday, and Sunday when calculating the quantity ofWP1432 to send in the replenishment order. This aspect is importantbecause sales for the days of Friday, Saturday, and Sunday may farexceed sales of the days of Monday, Tuesday, and Wednesday, forinstance.

It should be stressed that planogram information and store trait dataare continuously being monitored by the VMI system and in that regardwhen an effective date becomes equal to the current date, the newplanogram information and/or store trait data are put into effect.

Thus, specific embodiments and applications of a collaborative demandplanning and replenishment system have been disclosed. It should beapparent, however, to those skilled in the art that many moremodifications besides those already described are possible withoutdeparting from the inventive concepts herein. The inventive subjectmatter, therefore, is not to be restricted except in the spirit of theappended claims. Moreover, in interpreting both the specification andthe claims, all terms should be interpreted in the broadest possiblemanner consistent with the context. In particular, the terms “comprises”and “comprising” should be interpreted as referring to elements,components, or steps in a non-exclusive manner, indicating that thereferenced elements, components, or steps may be present, or utilized,or combined with other elements, components, or steps that are notexpressly referenced.

1. A method for generating replenishment orders for first and secondproducts at a retail store, comprising: a vendor managed inventorysystem storing retail-level planogram information having fixtureidentifiers; the vendor managed inventory system receiving store-levelcapacity information at a fixture level regarding first and secondfixtures where the fixtures are referenced by fixture identifiers; thevendor managed inventory system electronically receiving-store-levelsales performance information regarding sales of the first and secondproducts from the first fixture referenced by the fixture identifiers;the vendor manager inventory system generating store-level mapped databy matching fixture identifiers in the capacity information and salesperformance with the planogram information; the vendor managed inventorysystem determining fixture-level relative replenishment levels for thefirst and second products as a function of the store-level mapped data;triggering a fixture-level replenishment order when a fixture-levelperpetual quantity of the first and second products fall below adetermined fixture-level safety stock for the first fixture relative tothe to second fixture; and enabling a vendor to provide thereplenishment levels of the first and second products to the store. 2.The method of claim 1, further comprising vendor managed inventorysystem determining the safety stocks for the first and second productswith respect to the fixture level.
 3. The method of claim 1, furthercomprising the vendor managed inventory system receiving additionalsales performance information regarding sales of the first product fromthe second fixture, and using the vendor managed inventory system todetermine relative replenishment levels for the first product withrespect to the first and second fixtures.
 4. The method of claim 1,wherein the vendor managed inventory system additionally determines therelative replenishment levels at least in part using demographic data.5. The method of claim 1, wherein the vendor managed inventory systemadditionally determines the relative replenishment levels with respectto a week at least in part as a function of a day of the week.
 6. Themethod of claim 5, wherein the vendor managed inventory systemadditionally determines the relative replenishment levels at least inpart as a function of a specific future date.
 7. The method of claim 1,wherein the vendor managed inventory system additionally determines therelative replenishment levels at least in part as a function of anhistoric trend in product demand.
 8. The method of claim 1, wherein thevendor managed inventory system additionally determines the relativereplenishment levels at least in part as a function of an expected trendin product demand.
 9. The method of claim 1, wherein the step of thevendor managed inventory system determining relative replenishmentlevels comprises analyzing fixture level point of sale data.